Graph structured data are widely existed and applied in the real-world
a...
Although powerful graph neural networks (GNNs) have boosted numerous
rea...
Molecular property prediction is an important problem in drug discovery ...
Making personalized recommendation for cold-start users, who only have a...
Graph Neural Networks (GNNs) have been recently introduced to learn from...
Privacy and security concerns in real-world applications have led to the...
In recent years, Graph Neural Networks (GNNs) have been popular in the g...
Over these years, multi-agent reinforcement learning has achieved remark...
In recent years, Graph Neural Networks (GNNs) have been popular in graph...
Completing missing facts is a fundamental task for temporal knowledge gr...
Feature extractor plays a critical role in text recognition (TR), but
cu...
Spatiotemporal activity prediction, aiming to predict user activities at...
Scoring function (SF) measures the plausibility of triplets in knowledge...
Aiming at two molecular graph datasets and one protein association subgr...
Due to the success of Graph Neural Networks (GNNs) in learning from
grap...
Nonconvex regularization has been popularly used in low-rank matrix lear...
While hyper-parameters (HPs) are important for knowledge graph (KG) lear...
Graph structured data is ubiquitous in daily life and scientific areas a...
Federated optimization (FedOpt), which targets at collaboratively traini...
Short text classification is a fundamental task in natural language
proc...
Graph classification is an important problem with applications across ma...
Tabular data prediction (TDP) is one of the most popular industrial
appl...
Reasoning on the knowledge graph (KG) aims to infer new facts from exist...
Scoring functions, which measure the plausibility of triples, have becom...
Collaborative filtering (CF), as a fundamental approach for recommender
...
The scoring function, which measures the plausibility of triplets in
kno...
Tensor, an extension of the vector and matrix to the multi-dimensional c...
N-ary relational knowledge bases (KBs) represent knowledge with binary a...
Recent years have witnessed the popularity and success of graph neural
n...
Recently, a special kind of graph, i.e., supernet, which allows two node...
Since convolutional neural networks (ConvNets) can easily memorize noisy...
Classical machine learning implicitly assumes that labels of the trainin...
Graph neural network (GNN) has recently been established as an effective...
Non-local low-rank tensor approximation has been developed as a
state-of...
Negative sampling, which samples negative triplets from non-observed one...
Negative sampling approaches are prevalent in implicit collaborative
fil...
Recent years have witnessed the popularity of Graph Neural Networks (GNN...
Matrix learning is at the core of many machine learning problems. To
enc...
With the rapid development of knowledge bases (KBs), link prediction tas...
Scene text recognition (STR) is very challenging due to the diversity of...
Knowledge graph (KG) embedding is a fundamental problem in mining relati...
Sample-selection approaches, which attempt to pick up clean instances fr...
Interaction function (IFC), which captures interactions among items and
...
Neural architecture search (NAS) recently attracts much research attenti...
Robustness recently becomes one of the major concerns among machine lear...
Knowledge graph embedding (KGE) aims to find low dimensional vector
repr...
The quest of `can machines think' and `can machines do what human do' ar...
Knowledge Graph (KG) embedding is a fundamental problem in data mining
r...
Non-local low-rank tensor approximation has been developed as a
state-of...
In many practical machine-learning applications, it is critical to allow...